College of Engineering and Computing, University of South Carolina, Columbia, SC 29208, USA.
Phys Chem Chem Phys. 2020 May 28;22(20):11174-11196. doi: 10.1039/d0cp00972e. Epub 2020 May 12.
High throughput experimentation in heterogeneous catalysis provides an efficient solution to the generation of large datasets under reproducible conditions. Knowledge extraction from these datasets has mostly been performed using statistical methods, targeting the optimization of catalyst formulations. The combination of advanced machine learning methodologies with high-throughput experimentation has enormous potential to accelerate the predictive discovery of novel catalyst formulations that do not exist with current statistical design of experiments. This perspective describes selective examples ranging from statistical design of experiments for catalyst synthesis to genetic algorithms applied to catalyst optimization, and finally random forest machine learning using experimental data for the discovery of novel catalysts. Lastly, this perspective also provides an outlook on advanced machine learning methodologies as applied to experimental data for materials discovery.
高通量实验在多相催化中提供了一种有效的解决方案，可以在可重复的条件下生成大量数据集。从这些数据集中提取知识主要使用统计方法，旨在优化催化剂配方。将先进的机器学习方法与高通量实验相结合，具有巨大的潜力，可以加速对新型催化剂配方的预测发现，而这些新型催化剂配方在当前的实验统计设计中并不存在。本观点从催化剂合成的统计实验设计到应用于催化剂优化的遗传算法，再到使用实验数据发现新型催化剂的随机森林机器学习，选择了一些具有代表性的例子进行了描述。最后，本观点还展望了将先进的机器学习方法应用于材料发现的实验数据。
